In his article Big Data and Pharmacovigilance: Using Health Information Exchanges to Revolutionize Drug Safety, which is forthcoming in the Iowa Law Review, Ryan Abbott argues that third parties, including academics, insurance companies, and rival drug companies, should be incentivized via an “administrative bounty proceeding” to analyze the large and rich datasets that will be generated by health information exchanges. Should a third party’s original statistical analysis reveal safety or efficacy concerns about a drug, Abbott suggests, it could submit the results to the Food and Drug Administration and be paid a taxpayer-funded bounty, the amount of which would be based on the value of the new information to the government in terms of health care dollars saved. If a drug’s manufacturer knew or should have known about the concerns brought to light by the third party, Abbott proposes that the manufacturer fund the bounty, the amount of which would be based on the drug’s sales; depending on its degree of culpability, a manufacturer could even be liable to both the third party and the government for damages.

Abbott believes that the bounty system he proposes would level the pharmacovigilance playing field in a way that would redound to the benefit of consumers. In his words: “The public deserves an advocate as equally committed to challenging the safety and efficacy of approved drugs as product sponsors are to maintaining these drugs on the market.” Writing about Abbott’s proposal at the Bill of Health blog, Dov Fox distills it down to the following provocative question: Are we “better off evaluating medicines under an inquisitorial system or an adversarial system”?

I also recommend Jennifer Herbst’s article How Medicare Part D, Medicaid, Electronic Prescribing and ICD-10 Could Improve Public Health (but Only if CMS Lets Them), which is forthcoming in Health Matrix: Journal of Law-Medicine. While the title might seem daunting, the article itself brings clarity to a murky, highly-technical area of the law with enormous significance for public policy. As Herbst explains, although both Medicare Part D and Medicaid limit reimbursement to drugs prescribed for “medically accepted indications,” this limitation is not enforced, at least not at the time of payment. And, while the government’s attempts to enforce it retroactively have led to headline-making settlements with pharmaceutical companies, they have not resulted in a significant dent in the rate of unscientifically-supported prescribing.

Herbst recommends that the government take advantage of the inroads made by electronic prescribing and require that patient diagnosis codes be made a condition of payment for outpatient prescription drugs. Linking drugs to diagnoses in this way would allow pharmacists to do a more thorough safety review of the prescriptions they fill and it would give the government a powerful pharmacovigilance tool. Of course, it would also allow the government to decline to provide reimbursement for drugs prescribed for indications that are not “medically accepted.” Herbst argues that this would be a mistake because it could lead to widespread miscoding – there’s a disconnect between what the government deems medically accepted and what providers consider sound medical practice – which would undermine the value of the data being collected. I wonder, however, whether it would be politically feasible for the Centers for Medicare & Medicaid Services “to continue its current policy of paying for all outpatient prescriptions not subject to prior authorization (contrary to the letter of the Medicare Part D and Medicaid statutes)” in the face of the data Herbst’s proposal would generate.